Academic Journal
Self-Difference Convolutional Neural Network for Facial Expression Recognition
| العنوان: | Self-Difference Convolutional Neural Network for Facial Expression Recognition |
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| المؤلفون: | Leyuan Liu, Rubin Jiang, Jiao Huo, Jingying Chen |
| المصدر: | Sensors, Vol 21, Iss 2250, p 2250 (2021) |
| بيانات النشر: | MDPI AG |
| سنة النشر: | 2021 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | facial expression recognition, difference-based method, self-difference convolutional neural network, facial expression synthesis, facial expression classification, Chemical technology, TP1-1185 |
| الوصف: | Facial expression recognition (FER) is a challenging problem due to the intra-class variation caused by subject identities. In this paper, a self-difference convolutional network (SD-CNN) is proposed to address the intra-class variation issue in FER. First, the SD-CNN uses a conditional generative adversarial network to generate the six typical facial expressions for the same subject in the testing image. Second, six compact and light-weighted difference-based CNNs, called DiffNets, are designed for classifying facial expressions. Each DiffNet extracts a pair of deep features from the testing image and one of the six synthesized expression images, and compares the difference between the deep feature pair. In this way, any potential facial expression in the testing image has an opportunity to be compared with the synthesized “Self”—an image of the same subject with the same facial expression as the testing image. As most of the self-difference features of the images with the same facial expression gather tightly in the feature space, the intra-class variation issue is significantly alleviated. The proposed SD-CNN is extensively evaluated on two widely-used facial expression datasets: CK+ and Oulu-CASIA. Experimental results demonstrate that the SD-CNN achieves state-of-the-art performance with accuracies of 99.7% on CK+ and 91.3% on Oulu-CASIA, respectively. Moreover, the model size of the online processing part of the SD-CNN is only 9.54 MB (1.59 MB |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1424-8220 |
| Relation: | https://www.mdpi.com/1424-8220/21/6/2250; https://doaj.org/toc/1424-8220; https://doaj.org/article/6c5250e878274a688579fff84f7382be |
| DOI: | 10.3390/s21062250 |
| الاتاحة: | https://doi.org/10.3390/s21062250 https://doaj.org/article/6c5250e878274a688579fff84f7382be |
| رقم الانضمام: | edsbas.628ED5BE |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 14248220 |
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| DOI: | 10.3390/s21062250 |